System of performing a retrospective drug profile review of de-identified patients

ABSTRACT

An apparatus and method for delivering targeted informational messages includes a computer system for creating a de-identified encrypted PID and de-identified patient transaction data in a retail store for transmission and storage. A subset of de-identified encrypted PIDs are associated with targeted informational messages by the system and transmitted to retail stores, where a targeted message is printed on behalf of the patient corresponding to the de-identified encrypted PID.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a U.S. national stage entry of internationalapplication PCT/US06/19432 filed May 18, 2006, which claims priority toprovisional application no. 60/685,491 filed May 31, 2005, andnon-provisional application Ser. No. 11/235,083, filed Sep. 28, 2005,the contents of all of which are incorporated herein by reference.

FIELD OF THE INVENTION

The invention relates to providing specific advisory messages topatients/consumers in a store, pharmacy or other location.

DISCUSSION OF THE BACKGROUND

U.S. Pat. No. 6,067,524 to Byerly et al. discloses a method and systemfor generating advisory messages to pharmacy patients. The teachings ofU.S. Pat. No. 6,067,524 are incorporated herein by reference.

SUMMARY OF THE INVENTION

Definitions

We refer to a Computer System with the acronym CS.

Certain terms used in this application are defined below. In some cases,examples are provided to clarify the definition.

A consumer, in this application, is synonymous with a patient, or apurchaser of a drug, or one who is prescribed a drug, or one who takes adrug, or one who fills a prescription for a drug or a user of a drug;all of these terms are synonymous with each other.

NDC is an acronym for National Drug Code. Each medication listed underSection 510 of the U.S. Federal Food, Drug, and Cosmetic Act is assigneda unique 11-digit, 3-segment number. This number, known as the NationalDrug Code (NDC), identifies the labeler/vendor, product, and tradepackage size. The first segment, the labeler code, is assigned by theFDA. A labeler is any firm that manufactures (including repackers orrelabelers), or distributes (under its own name) the drug.

PID is an acronym for Patient Identification. PID in this applicationrefers to any unique set of symbols that identifies a particularpatient. PID is an acronym for “Patient ID.” A PID may, for example, becomprised of a sequence of numbers and letters.

POS is an acronym for Point Of Sale. A POS is the area where a consumerengages in transactions at a retail store.

A POS terminal, in this application, means a point of sale terminal,which is an input output device for communicating consumer transactioninformation between a consumer and a retail store to a CS associatedwith the retail store.

A POS CS, in this application, means a CS for logging POS transactiondata, including any peripheral and input and output devices connected toit, such as POS terminals, optical scanners, printers, etc.

All databases herein may be formatted as one or more files, xmldocuments, relational database files, and may include tables, forms,queries, relations, reports, modules, and other objects useful indatabase management and programming. All computers herein may include adigital central processing unit, RAM memory, disk drives, operatingsystem software, and conventional hardware and software to implement,for example, database management and networking.

A product code, in this application, is a code associated with a productassigned by a manufacturer or distributor (labeler). For example, aproduct code may be a code assigned by a company, a store, or by anindustry standard, to a product.

A prescription, in this application, means an order for the preparationand administration of a medicine or drug.

A purchase, in this application, means a transaction involving at leasttwo parties in which forms of payment such as cash, check, charge,debit, smart card, gift card, credit slip, or credit is exchanged forone or more goods or services in a retail store.

Purchase data, in this application, means data associated withpurchases. For example, purchase data may include a product code for theproduct purchased, product description, product purchase list price,actual price paid, date of purchase, time of purchase, transaction ID,location of purchase, discount amount, discount type, and type ofpayment, and a PID.

A retail store, in this application, refers to a store in which productsare located and sold to consumers. Examples of retail stores includepharmacies, supermarkets, quick service restaurants, convenience stores,retail clothing stores, gas stations, petroleum stores, wholesalers,outlet stores, and warehouses.

An individual transaction, in this application, means a single exchangeinvolving at least two legal entities. A purchase is an individualtransaction.

Individual transaction data, in this application, means data associatedwith an individual transaction.

Transaction data, in this application, means data associated with one ormore transactions. For example, transaction data may include purchasedata, time and date data, PIDs, transaction terminal IDs, store IDs forone or more transactions.

Transaction ID, in this application, refers to a unique identificationassociated with an individual transaction.

Switch, in this application, refers to a claim adjudication legal entitythat accepts individual transaction data from a pharmacy, submits thedata to an insurance company reformatted to the data requirements of theinsurance company, receives responses thereto from the insurancecompany, and forwards those responses to the originating pharmacy.

A pharmacy management CS, in this application, refers to a POS CSincluding a patient database and a drug database that is capable oflogging transaction data for consumers in a pharmacy.

A drug database, in this application, refers to a database including atleast three of the following: patient names; prescribing history records(e.g., drug, dosage, doctor, date); patient method of payment (e.g.,cash, check, credit, or health insurance company); group plan name andmember ID; name and address of primary doctor and DEA number of primarydoctor; association of prescription to prescribing doctor's ID andcontact information; and a drug visualization system to view drug imagesagainst actual pills or capsules.

De-identifying patient information means removing sufficient key itemsfrom the patient information such that the information cannot be used,alone or in combination with other reasonably available patientinformation, to identify the individual patient.

Re-identify means to take de-identified patient information and assignit to the identity of the patient.

The SHA-1 Standard defines the Secure Hash Algorithm, SHA-1, forcomputing a condensed representation of a message or a data file. When amessage of any length <264 bits is input, the SHA-1 produces a 160-bitoutput called a message digest. SHA-1 is described in FederalInformation Processing Standards Publication 180-1, Apr. 17, 1995, theentire contents of which are incorporated herein by reference. The SHA-1is designed to have the following properties: it is computationallyinfeasible to determine a message which corresponds to a given messagedigest, or to determine two different messages which produce the samemessage digest.

The Health Insurance Portability and Accountability Act of 1996 (HIPAA)confirms standards for regulatory approved de-identification. HIPAAapproved de-identification requires removal of identifiers for thepatient, the patient's relatives, employers, and household members. Thetest for HIPAA approved de-identification is that “a person withappropriate knowledge of and experience with generally acceptedstatistical and scientific principles and methods for rendering patientinformation not individually identifiable” determines that the risk isvery small that the patient information could be used, alone or incombination with other reasonably available patient information, toidentify a patient and documents the analysis to justify thisdetermination.

HIPAA approved de-identification thus may involve the deletion oralteration of some portion of patient data to protect patient privacy,while preserving the overall statistical and analytical integrity of thedata. This is due to the fact that other patient information such asdemographics, medical information, and healthcare facility informationcould be used separately or in combination to discern the identity ofsome patients.

The safe harbor method for de-identification, in this application, meansthe method defined by HIPAA. The safe harbor method requires (1) theremoval of a list of 18 enumerated patient identifiers and (2) no actualknowledge that the patient information remaining could be used, alone orin combination, to identify the patient. The patient identifiers thatmust be removed include direct patient identifiers, such as name, streetaddress, social security number, as well as other patient identifiers,such as birth date, admission and discharge dates, and five-digit zipcode. The safe harbor method also requires removal of geographicsubdivisions smaller than a state, except for the initial three digitsof a zip code if the geographic unit formed by combining all zip codeswith the same initial three digits contains more than 20,000 people. Thesafe harbor method does not require the removal of age if less than 90,gender, ethnicity, and other demographic information.

Objects

It is an object of the present invention to provide to patientsinformation that motivates the patients to comply with specified medicaltreatments and to educate the patient regarding the medications.

It is an object of the present invention to enable pharmacies and otherparties (e.g., pharmaceutical companies, consumer packaged goodsmanufacturers, and retailers) to define, develop, and deliveradvertising programs targeted at specific groups of patients.

These and other objects are provided by a novel system and method forproviding targeted informational messages to individuals, comprising apharmacy management CS configured to receive individual transaction dataand an associated non-encrypted PID, to de-identify the individualtransaction data to produce a de-identified individual transaction data,to encrypt said non-encrypted PID to produce an encrypted PID, and touse the encrypted and de-identified data to generate a targetedinformational message and deliver that message to the person associatedwith the PID.

The encryption algorithm produces the same encrypted PID whenever thesame un-encrypted PID is input.

The novel system also includes a CS configured to determine from thede-identified transaction data for the transaction associated with theencrypted PID at least one targeted informational message, store thetargeted informational message in association with the encrypted PID,and transmit to the POS the targeted informational message in responseto receipt of the PID at the POS.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is described in connection with the following figures,wherein like reference numerals designate identical or correspondingparts.

FIG. 1 is a high level schematic diagram illustrating novel system 1;

FIG. 2 shows computer 202, terminal 206, printer 208, database 130A, andline 104.

FIG. 3A shows data structure 300A for database 130A of FIG. 1;

FIG. 3B shows data structure 300B for databases 120A and 130A of FIG. 1;

FIG. 3C shows data structure 300C for databases 120A and 130A of FIG. 1;

FIG. 4 shows element 402, retail system 404, element 412, newsletter410, pharmacy publication system 406, line 104, line 112, local retailerspecial content database server 430, and element 422.

FIG. 5 is a flow diagram showing flow of data in computer equipmentassociated with one embodiment of either system 120 or system 130 ofFIG. 1;

FIG. 6 is a flow diagram showing flow of data in computer equipmentassociated with element 516 of FIG. 5;

FIG. 7 is a flow chart of a method of using the system shown in FIG. 1;

FIG. 8 is a flow chart of a method of using the system shown in FIG. 1;

FIG. 9 illustrates an example database schema of database 120A;

FIG. 10 illustrates an example database schema of database 120A;

FIG. 11 illustrates an example database schema of database 120A;

FIG. 12 illustrates an example database schema of database 120A;

FIGS. 13-25 are flowcharts illustrating methods of utilizing thedatabase schema of database 120A.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 shows pharmacy management CS 130, pharmacy management CS database130A, network 110, communication links 104, central CS 120, central CSdatabase 120A, insurance company CS 140, insurance company CS database140A, switch CS 142, and switch CS database 142A.

The communication links 104 indicate a means for data transmissionincluding wire and wireless transmission hardware, data format, andtransmission protocols. Lines connecting a database to a CS indicatesthat the computer controls read and write access to that database.

FIG. 3A shows data structure 300A including PID table 300A-2 andtransaction data table 300A-1. PID table 300A-2 includes data fieldsassociated with a PID that identify the patient, including postaladdress field 350, email address field 352, name field 358, telephonenumber field 356 and other data fields that can be used to identify apatient. Transaction table 300A-1 includes data associated withtransactions in a pharmacy including PID field 330, store ID field 310,date field 334, NDC field 336, quantity field 338, and physician field340. Tables 300A-1 and 300A-2 are representative of identification dataand transaction data stored in pharmacy management CS 130's database130A. Data stored in data structure 300B is the result of de-identifyingdata stored in data structure 300A.

FIG. 3B shows data structure 300B including encrypted PID field 302, andtransaction data fields 310, 334, 336, 338, and other non-identifyingtransaction data fields.

Data structure 300B may be used, for example, as a format for sendingtransaction data records from pharmacy management CS 130 to central CS120. Alternatively, the data structures and functionality described asexisting in central CS 120 may reside in the system 130 on the samecomputer or distributed between computers interconnected by a network.

In alternative embodiments data structure 300A may be used to transmittransaction data to central CS 120. Central CS 120 de-identifies thetransaction data and stores the de-identified data in database 120Ausing data structure 300B.

FIG. 3C shows targeted message data structure 300C of central CS 130associating encrypted PID field 320 and targeted message field 322. Datastructure 300C may reside on the CSs 120 and 130. Data structure 300Cmay be used as a format for transmission of data between systems 120 and130.

FIGS. 5-6 diagram flow of data in a specific embodiment or closelyrelated embodiments.

Pharmacy publication system 406 transmits patient B's de-identifiedtransaction data and patient B's encrypted PID to data load system 510via local link 112 or network link 104.

Any secure one-way algorithmic encryption process or encrypting hashalgorithm that takes a unique unencrypted sequence of symbols (e.g.,numbers or letters) as input and produces a unique encrypted sequence ofsymbols as output, such that the same unique encrypted output isproduced for a given unique input, may be used as an alternative to theSHA-1 algorithm.

FIG. 5 is a flow diagram showing the flow of data preferably in centralCS 120, or alternatively in pharmacy management CS 130. FIG. 5 showsdata load system 510, data warehouse 514, categorization and filteringsystem 516, database build system 520, publication content databaseserver 512, and retailer specific content database server 522.

In exemplary embodiments, data load system 510, data warehouse 514,categorization and filtering system 516, database build system 520,publication content database server 512, and retailer specific contentdatabase server 522 may be structured as sub-systems all residing withina single computer, or, alternately, may be structured as a physicallydistributed CS interconnected by conventional communication hardware andsoftware.

In operation, data load system 510 receives as input SHA-1 encryptedPIDs linked to de-identified transaction data from pharmacy publicationsystem 406 via data network link 104 or local data link 112. Data loadsystem 510 transforms de-identified patient data into a structureconsistent with the data warehouse 514 requirements. Data load system510 then outputs de-identified patient data including an SHA-1 encryptedPID to data warehouse 514. Data load system 510 may, for example,populate tables in the data warehouse schema and then verify that thedata is ready for use. Data load system 510 may, for example, verify thereferential integrity between tables to ensure that all records relateto appropriate records in other tables.

In operation, data warehouse 514 functions, for example, as a datarepository for organizing, structuring and storing plural pharmacies'individual transaction data for query and analysis. Data warehouse 514implements a process by which large quantities of related data from manyoperational systems is merged into a single, standard repository toprovide an integrated information view based on logical queries. Forexample, data warehouse 514 may be a repository of 65 weeks ofindividual transaction data from 12,500 retail stores including thereina large number that either are pharmacies or include therein pharmacies,and store therein de-identified historical patient profiles.

Types of logical queries may relate to “data mining,” which can bedefined as a process of data selection, exploration and building modelsusing vast data stores to uncover previously unknown patterns. Otherqueries may be in support of research on a particular subject. Inoperation, data warehouse 514 serves as a tool that can provideinformation for use in a wide variety of therapeutic, statistical, andeconomic analyses and interventions to aid in making healthcare andbusiness related decisions. In operation, data warehouse 514 can alsogenerate and store feedback regarding the impact of prior decisions,facilitating improvements in patient care, operational efficiency, andreducing the cost of medical care.

In operation, categorization and filtering system 516 formulates andexecutes DBMS (Data Base Management System) queries on the de-identifiedindividual transaction data residing on data warehouse 514, using forexample, SQL boolean logic and filtering operations. Categorization andfiltering system 516 filters the query results to produce a subset ofencrypted PIDs linked to de-identified individual transaction datamatching categorization and filtering criteria. If the de-identifiedindividual transaction data for one or more transactions linked to anencrypted PD matches certain categorization and filtering criteria, thenthe linked encrypted PID is termed a qualified encrypted PID.Categorization and filtering system 516 outputs a certain set ofqualified encrypted PIDs to database build system 520.

In operation, categorization and filtering system 516 may implement DBMSselection operations based upon complex criteria. These operations maybe implemented as a series of simple queries using, for example,relatively simple SQL boolean logic, selection, and filteringoperations. Such a series of simple queries may result, for example, ina series of intermediate tables or work tables that are progressivelymore refined and contain progressively smaller subsets of qualifyingrecords. Partitioning the query tasks of categorization and filteringsystem 516 in this way may result in increased database accessefficiency and shorter processing times. Partitioning the categorizationand filtering operation into a series of simple query operations alsopromotes ease of programming, maintenance, modification, and testing.

In operation, database build system 520 receives as input fromcategorization and filtering system 516 a certain set of qualifiedencrypted PIDs. Database build system 520 queries publication contentdatabase server 512 for targeted messages associated with that certaincategorization and filtering criteria. The retrieved targeted messagesare those associated with the certain categorization and filteringcriteria used to produce the certain set of qualified encrypted PIDs.Database build system 520 associates the retrieved targeted messageswith the set of encrypted PIDs, for example, by combining the qualifiedencrypted PIDs output from categorization and filtering system 516 withthe certain targeted messages retrieved from publication contentdatabase server 512. Database build system 520 outputs targeted messageslinked to qualified encrypted PIDs to retailer specific content databaseserver 522. Preferably, the output also associates store ID or retailerID with the encrypted PID. Database build system 520 may, for example,create and populate tables of targeted messages linked to qualifiedencrypted PIDs on retailer specific content database server 522.

In operation, publication content database server 512 services queriesfrom database build system 520 for targeted messages.

In operation, retailer specific content database server 522 receives asinput updates of retailer specific targeted newsletter content fromdatabase build system 520. Retailer specific content database server 522functions to provide updated retailer specific targeted newslettercontent to pharmacy publication system 406 via data links 104 and 112and network 110 using, for example, File Transfer Protocol (FTP).

In exemplary embodiments, the functions of central CS 120 necessary togenerate advisory messages associated with encrypted PIDs may beperformed by suitably configured embodiments of pharmacy management CS130.

FIG. 6 is a flow diagram showing the flow of data in categorization andfiltering system 516. FIG. 6 shows categorization system 610 andfiltering system 620. In exemplary embodiments, categorization system610 and filtering system 620 may be structured as sub-systems allresiding within a single computer, or, alternately, may be structured asa physically distributed CS interconnected by conventional communicationhardware and software.

FIG. 6 also shows categories 612 which are outputs of categorizationsystem 610 and inputs to filtering system 620. FIG. 6 also showsde-identified patient data output 628 of data warehouse 514 as input tofiltering system 620. FIG. 6 also shows filtering system 620 outputtingfiltered de-identified patient data 622.

FIG. 7 shows a flowchart depicting a method for operating system 1.

In step 704, patient PIED and transaction data are received at pharmacymanagement CS 130. For example, the patient's name may be input atretail system 404 and the patient's PID then retrieved from a database.

In step 706, pharmacy management CS 130 encrypts the PID andde-identifies the individual transaction data.

In step 708, pharmacy management CS 130 transmits de-identifiedtransaction data linked to encrypted patient PID 422 to central CS 120.

In step 710, the central CS 120 updates its data store of individualtransaction data records associated with the encrypted PD by adding thenewly received individual transaction data thereto.

In step 712, central CS 120 matches the encrypted PID with a targetedmessage using the encrypted PID as the search key.

For example, central CS 120 identifies a safety warning relating to adrug previously purchased by the patient having the encrypted PID, andassociates that warning with the encrypted PID.

For another example, central CS 120 identifies a brand of a first drugpreviously purchased by the patient associated with the encrypted PIDand associates with the encrypted PID marketing material for a differentbrand of the same drug or a brand of a different drug used for the sameclinical indication as the first drug with the encrypted PID.

For another example, the central CS 120 identifies lack of purchase in apattern of prior purchases of a first drug or drugs having the sameclinical indication as the first drug and associates with the encryptedPID a targeted message identifying at least one brand of a drug or drugshaving that clinical indication.

In step 714, central CS 120 transmits a targeted message with anencrypted PID to pharmacy management CS 130.

In step 716, pharmacy management CS 130 prints targeted newsletter 410in response to the receipt of the PID corresponding to the encrypted PIDor in response to a transaction including that PID.

FIG. 8 shows a method of using system 120.

In step 802, categorization system and filtering system 516 linksspecific drugs to specific disease categories. For example, the drugLipitor is associated with the disease category “high cholesterolpatients” and the drug insulin is associated with associated with thedisease category “diabetes patients.”

In step 804, categorization system and filtering system 516 linksde-identified data in data warehouse 514 to specific disease categories.For example, if a de-identified data record indicates that insulin hasbeen purchased in the past, then that de-identified data record isallocated to the category “diabetes patients.”

In step 806, categorization system and filtering system 516 furtherfilters the de-identified data records allocated to patient categoriescreated in step 804 to produce targeted subsets. For example, system 516creates one subset for “high cholesterol patients” and one subset for“diabetes patients,” or subsets for patients in both categories or onlyone of each category. For example, the operations of step 806 may beimplemented as a series of DBMS queries using, for example, SQL booleanlogic, selection, and filtering operations. Such a series of simplequeries may result, for example, in a series of intermediate, or work,tables, that are progressively more refined and contain progressivelysmaller subsets of qualifying de-identified data records.

In step 808, database build system 520 links the targeted de-identifieddata record subsets produced in step 806 with appropriate targetedmessages from publication content database server 512 where, forexample, one targeted message for patients of both categories and oneeach for patients having only one of diabetes and high cholesterol. Forexample, database build system 520 may extract the encrypted PID from ade-identified data record and link the encrypted PID to an appropriatetargeted publication.

In step 810, database build system 520 updates retailer specific contentdatabase server 522 with targeted publications data linked to encryptedPIDs, and the store Ds with which the encrypted PIDs are eachassociated.

Embodiments target for informational communication subsets ofde-identified patients whose individual de-identified transaction datasatisfies complex selection criteria Exemplary embodiments may targetfor an informational communication for example the following subsets ofde-identified patients (A)-(N):

(A) Patients on two or more medications that clarify the exact diseasefor which they are being treated that a single medication does notproperly identify;

(B) Patients taking two or more medications over time indicating theirdisease is requiring additional treatment to maintain or control itsprogression;

(C) Patients taking a sequence of drugs indicating what stage of therapywithin the patient's disease, patient currently is in;

(D) Patients not currently being treated for a particular condition butwho are likely candidates for drug therapy due to one or more riskfactors as defined by other medications within the patients' drugprofile;

(E) Patients who are already compliant and or persistent on theirmedication regimen as defined by consistent use of drug therapy overtime;

(F) Patients who have already switched from one medication within a drugclass to another medication within same or different drug class known totreat the same condition;

(G) Patients who are using medications for chronic treatment vs. acutetreatment for a particular disease by identifying patients receivingmultiple new prescriptions for the same drug over time;

(H) Patients that should be eliminated from a patient subset due to aknown drug contraindication as identified by additional drug therapycreating the contraindication;

(I) Patients that should be eliminated from a patient subset due to aknown drug interaction as identified by additional drug therapy creatingthe drug interaction;

(J) Patients that have stopped taking their current medication asidentified by being late for a prescription refill and reminding them tocontinue their therapy;

(K) Patients that have stopped taking their current medication asidentified by being late for a prescription refill and inform them ofother medications used to treat the same condition that may work betterfor them;

(L) Patients who have previously used medications to treat seasonalconditions that could benefit from similar medication therapy upon thenext seasonal event;

(M) Patients who may be taking two drugs in combination that couldbenefit from taking one drug containing both individual drugingredients; and

(N) Patients who may benefit from a refill reminder just prior to theirprescription refill due date as identified by previous non-compliantprescription refill behavior.

FIGS. 9-14 disclose data schemas and algorithms useful in achieving thegoals of embodiments A-N.

FIG. 9 shows database tables included in central server database 120A.Database 120A may be implemented using a relational database usingseveral tables, for example. Alternatively, database 120A may beimplemented using Extensible Markup Language (XML) “tagged” data. Theembodiment illustrated in FIG. 9 has a relational database comprisinginterrelated database tables. The database of FIG. 9 is characterized bya schema, which includes a set of interrelations between the exemplarycomponent tables shown in FIG. 9.

Database 120A of FIG. 9 includes database table 902, database table 904,and database table 300B′. FIG. 9 shows data fields of database table300B′ including encrypted PID field 302, and transaction data fieldsstore ID field 310, date field 334, NDC field 336, and quantity field338, and other non-identifying transaction data fields. Database table902 of FIG. 9 includes medication field 908 and NDC fields 906 . . . 932for NDC1 to NDCn respectively. As explained above, the NDC is typicallya unique 11-digit, 3-segment number identifying the labeler/vendor,product, and trade package size of a medication. However, the NDCs maybe represented in other ways. In each record, the NDCs identified infields 906 . . . 932 are associated with the medication identified infield 908. FIG. 9 shows one-to-many relation 922 relating NDC field 336of 300B′ to NDC fields 906 . . . 932 of database table 902.

Database table 904 of FIG. 9 includes disease field 910 and medicationfields 940 . . . 960. For each record in table 904, the medicationsidentified in fields 940 . . . 960 are associated with the treatment ofthe disease identified in field 910.

One-to-many relation 920 of FIG. 9 relates medication field 908 ofdatabase table 902 to medication fields 940 . . . 960 of database table910.

FIG. 10 shows an alternative schema of central server database 120A.Database 120A includes database table 1002, database table 1004,database table 1070, and database table 300B′. Database table 1002includes medication field 1006 and NDC fields 1008 through 1018.Database table 1004 includes medication field 1020 and risk factorfields 1022 through 1044. Risk factor fields 1022 through 1044 identifyrisk factors associated with the use of medications. Boolean values infields 1022 . . . 1044 indicate whether the risk factor 1022 . . . 1044is or is not associated with the medication identified in field 1020.Database table 1070 includes risk factor field 1050 and medicationfields 1052 through 1044. Medication fields 1052 through 1054 identifymedications associated with the risk factor. Boolean values in fields1052 . . . 1054 indicate whether the medication 1052 . . . 1054 is or isnot associated with the risk factor identified in field 1050.

Fields 940 . . . 960 in table 904 and 906 . . . 932 in table 902 maystore boolean values indication whether the corresponding NDC is or isnot associated with the disease identified in field 910 or medicationidentified in field 908, respectively.

FIG. 11 shows database table 1150 for implementing database 120A′ usingan alternative database schema. FIG. 11 shows medication field 1102 anddisease fields 1104 . . . 1140.

Records 1170 . . . 1190 of database table 1150 have each have medicationdata field 1102 and disease data fields 1104 . . . 1140. Using thisschema, a “1” stored in disease data field 1104 of record 1170 meansthat disease 1 is treated using medication 1. Likewise, a “1” stored indisease data field 1108 of record 1170 means that disease 1 is treatedusing medication 3.

FIG. 12 shows database table 1250 for implementing database 120A′ usingan alternative database schema. FIG. 12 shows medication field 1202 andNDC fields 1204 . . . 1240.

Records 1260 . . . 1280 of database table 1250 have each have medicationdata field 1202 and NDC data fields 1204 . . . 1240. Using this schema,a “1” stored in NDC data field 1204 of record 1270 means that NDC 1designates medication 1. Likewise, a “1” stored in disease data field1208 of record 1270 means that NDC 3 also designates medication 1. Thismeans that NDC 1 has the same product segment value as NDC 3.

FIG. 13 is a flowchart showing use of database 120A as shown in FIG. 9to identify patients that tale two or more medications associated with aspecific disease.

In step 1302 code determines all NDCs associated with a specific patientusing data table 300B′.

Next, in step 1304, code determines a first set of all medicationsassociated with the first set of NDCs using data table 902.

Next, in step 1306, code determines a first set of diseases associatedwith at least two medications in the first set of medications using datain table 904.

Next, in step 1308, code writes the encrypted PID and the first set ofdiseases to a file in which the data structure associates the encryptedPID with the first set of diseases.

The foregoing steps may be repeated for each unique encrypted PID infield 1302.

Alternatively, conventional SQL commands may be used to achieve the sameassociation of encrypted PIDs and diseases.

At some point, code refers to a table or file in which differentinformational messages are associated with each encrypted PID set toassociate with the set of diseases previously associated with thatencrypted PID.

Embodiment (B) is any algorithm applied to the foregoing data thatselects those de-identified patients talking two or more medicationsover time. The preferred algorithm uses database commands and booleanlogic to determines that the disease afflicting these patients requiresadditional treatment to maintain or control its progression.

FIG. 14 is a flowchart illustrating a method for utilizing the databaseschema and database tables of central server database 120A shown in FIG.9 to produce targeted informational messages for de-identified patientstaking two or more medications over time.

In step 1402 code determines all NDCs associated with a specific patientusing data table 300B′.

Next, in step 1404, code determines a first set of all medicationsassociated with the first set of NDCs using data table 902.

Next, in step 1406, code determines de-identified patients taking two ormore medications over time in the first set of medications using data intable 904.

Next, in step 1408, code writes the encrypted PID and the two or moremedications to a file in which the data structure associates theencrypted PID with the two or more medications.

The foregoing steps may be repeated for each unique encrypted PD infield 1402.

Alternatively, conventional SQL commands may be used to achieve the sameassociation of encrypted PIDs and the two or more medications.

At some point, code refers to a table or file in which differentinformational messages are associated with each encrypted PID set toassociate with the set of two or more medications previously associatedwith that encrypted PID.

Embodiment (C) is any algorithm applied to the foregoing data thatselects those de-identified patients taking a sequence of drugs. Thisembodiment then uses an algorithm implemented with boolean logic thatdetermines what stage of therapy within the course the of patient'sdisease that the patient currently is in.

FIG. 15 is a flowchart illustrating a method for utilizing the databaseschema and database tables of central server database 120A shown in FIG.9 to produce targeted informational messages for de-identified patientstaking a sequence of drugs.

In step 1502 code determines all NDCs associated with a specific patientusing data table 300B′.

Next, in step 1504, code determines a first set of medicationsassociated with the set of NDCs using data table 902.

Next, in step 1506, code determines a second set of medicationsassociated with the set of NDCs using data table 902.

Next, in step 1508, code determines from table 300B′ de-identifiedpatients from step 1508 whose purchase dates for first and second setsof medications have specified time sequence using data in table 904.

Next, in step 1510, code writes the encrypted PID and the first andsecond sets of medications to a file in which the data structureassociates the encrypted PID with the first and second sets ofmedications.

The foregoing steps may be repeated for each unique encrypted PID infield 1502.

Alternatively, conventional SQL commands may be used to achieve the sameassociation of encrypted PIDs and medications.

At some point, code refers to a table or file in which differentinformational messages are associated with each encrypted PID set toassociate with the set of medications previously associated with thatencrypted PID.

Embodiment (D) is any algorithm applied to the foregoing data thatselects those de-identified patients not currently being treated for aparticular condition. This embodiment uses programmed logic to correlaterisk factors with a patient's use of certain medications which form thepatient's drug profile. This embodiment then uses an algorithmimplemented with boolean logic to determine which patients are likelycandidates for drug therapy due to the one or more risk factors linkedto the medications within the patient's drug profile.

FIG. 16 is a flowchart illustrating a method for utilizing the databaseschema and database tables of central server database 120A shown in FIG.9 to produce targeted informational messages for de-identified patientsnot currently being treated for a particular condition.

In step 1602 code determines all NDCs associated with a specific patientusing data table 300B′.

Next, in step 1604, code determines a first set of all medicationsassociated with the first set of NDCs using data table 902.

Next, in step 1606, code determines a set of diseases associated with atleast two medications in the set of medications using data in table 904.

Next, in step 1608, code determines those de-identified patients notcurrently being treated for a particular disease using data table 902.

Next, in step 1610, code correlates risk factors with a de-identifiedpatient's use of certain medications which form the patient's drugprofile.

Next, in step 1612, code determines de-identified patients who arelikely candidates for drug therapy due to the one or more risk factorslinked to the medications within the patient's drug profile.

The foregoing steps may be repeated for each unique encrypted PID infield 1302.

Alternatively, conventional SQL commands may be used to achieve the sameassociation of encrypted PIDs and diseases.

At some point, code refers to a table or file in which differentinformational messages are associated with each encrypted PID set toassociate with the set of diseases previously associated with thatencrypted PID.

Embodiment (E) is any algorithm applied to the foregoing data thatselects those de-identified patients who are consistent in their use ofdrug therapy over a period of time. This embodiment then uses analgorithm implemented with boolean logic to determine that this group iscompliant or persistent on their medication regimen as defined by theirconsistent use of drug therapy over time.

FIG. 17 is a flowchart illustrating a method for utilizing the databaseschema and database tables of central server database 120A shown in FIG.9 to produce targeted informational messages for de-identified patientswho are consistent in their use of drug therapy over a period of time.

In step 1702 code determines all NDCs associated with a specific patientusing data table 300B′.

Next, in step 1704, code determines a first set of all medicationsassociated with the first set of NDCs using data table 902.

Next, in step 1706, code determines selects those de-identified patientswho are consistent in their use of drug therapy over a period of timeusing data in table 904.

Next, in step 1708, code writes the encrypted PID and the first set ofmedications to a file in which the data structure associates theencrypted PID with the first set of medications.

The foregoing steps may be repeated for each unique encrypted PID infield 1302.

Alternatively, conventional SQL commands may be used to achieve the sameassociation of encrypted PIDs and medications.

At some point, code refers to a table or file in which differentinformational messages are associated with each encrypted PID set toassociate with the set of medications previously associated with thatencrypted PID.

Embodiment (F) is any algorithm applied to the foregoing data thatselects those de-identified patients who have switched medications froma first medication within a drug class to a second medication within thesame or a different drug class. This embodiment then uses an algorithmimplemented with boolean logic to determine if the second medication isknown to treat the same condition.

FIG. 18 is a flowchart illustrating a method for utilizing the databaseschema and database tables of central server database 120A shown in FIG.9 to produce targeted informational messages for de-identified patientswho have switched medications from a first medication within a drugclass to a second medication within the same or a different drug class.

In step 1802 code determines all NDCs associated with a specific patientusing data table 300B′.

Next, in step 1804, code determines a first set of all medicationsassociated with the first set of NDCs using data table 902.

Next, in step L806, code determines a first set of diseases associatedwith at least two medications in the first set of medications using datain table 904.

Next, in step 1808, code selects those de-identified patients who haveswitched medications from a first medication within a drug class to asecond medication within the same or a different drug class.

Next, in step 1810, code writes the encrypted PID and the first andsecond sets of medications to a file in which the data structureassociates the encrypted PID with the first and second sets ofmedications.

The foregoing steps may be repeated for each unique encrypted PD infield 1302.

Alternatively, conventional SQL commands may be used to achieve the sameassociation of encrypted PIDs and first and second sets of medications.

At some point, code refers to a table or file in which differentinformational messages are associated with each encrypted PID set toassociate with the sets of medications previously associated with thatencrypted PID.

Embodiment (G) is any algorithm applied to the foregoing data thatselects those de-identified patients receiving multiple newprescriptions for the same drug over time. This embodiment then uses analgorithm implemented with boolean logic to determine patients who areusing medications for chronic treatment of a particular disease andthose patients who are using those same medications for acute treatmentof that disease.

FIG. 19 is a flowchart illustrating a method for utilizing the databaseschema and database tables of central server database 120A shown in FIG.9 to produce targeted informational messages for de-identified patientsreceiving multiple new prescriptions for the same drug over time.

In step 1902 code determines all NDCs associated with a specific patientusing data table 300B′.

Next, in step 1904, code determines a first set of all medicationsassociated with the first set of NDCs using data table 902.

Next, in step 1906, code selects those de-identified patients receivingmultiple new prescriptions for the same drug over time using data intable 904.

Next, in step 1908, code writes the encrypted PID and the set ofmultiple new prescriptions for the same drug over time to a file inwhich the data structure associates the encrypted PID with the set ofmultiple new prescriptions for the same drug over time.

The foregoing steps may be repeated for each unique encrypted PID infield 1302.

Alternatively, conventional SQL commands may be used to achieve the sameassociation of encrypted PIDs and medications.

At some point, code refers to a table or file in which differentinformational messages are associated with each encrypted PID set toassociate with the set of medications previously associated with thatencrypted PID.

Embodiment (H) is any algorithm applied to the foregoing data thatselects those de-identified patients receiving a first drug whosubsequently receive a second drug. This embodiment uses programmedlogic to correlate risk factors with a patient's use of the first drugwith the second drug to determine if the additional drug therapy createsa contraindication. This embodiment then uses an algorithm implementedwith boolean logic to determine if these patients should be eliminatedfrom a patient subset due to a drug contraindication.

FIG. 20 is a flowchart illustrating a method for utilizing the databaseschema and database tables of central server database 120A shown in FIG.9 to produce targeted informational messages for de-identified patientsreceiving a first drug who subsequently receive a second drug.

In step 2002 code determines all NDCs associated with a specific patientusing data table 300B′.

Next, in step 2004, code determines a first set of all medicationsassociated with the first set of NDCs using data table 902.

Next, in step 2006, code correlate risk factors with a patient's use ofa first drug with a second drug to determine if the additional drugtherapy creates a contraindication using data in table 904.

Next, in step 2008, code writes the encrypted PID and the set ofmedications to a file in which the data structure associates theencrypted PID with the set of medications.

The foregoing steps may be repeated for each unique encrypted PID infield 1302.

Alternatively, conventional SQL commands may be used to achieve the sameassociation of encrypted PIDs and medications.

At some point, code refers to a table or file in which differentinformational messages are associated with each encrypted PID set toassociate with the set of medications previously associated with thatencrypted PID.

Embodiment (I) is any algorithm applied to the foregoing data thatselects those de-identified patients receiving a first drug whosubsequently receive a second drug. This embodiment uses programmedlogic to correlate risk factors associated with a patient's use of thefirst drug with the second drug to determine if the additional drugtherapy creates a potential drug interaction. This embodiment then usesan algorithm implemented with boolean logic to determine if thesepatients should be eliminated from a patient subset due to a potentialdrug interaction.

FIG. 21 is a flowchart illustrating a method for utilizing the databaseschema and database tables of central server database 120A shown in FIG.9 to produce targeted informational messages for de-identified patientsreceiving a first drug who subsequently receive a second drug.

In step 2102 code determines all NDCs associated with a specific patientusing data table 300B′.

Next, in step 2104, code determines a first set of all medicationsassociated with the first set of NDCs using data table 902.

Next, in step 2106, code selects those de-identified patients receivinga first drug who subsequently receive a second drug using data in table904.

Next, in step 2108, code correlates risk factors associated with apatient's use of the first drug with the second drug to determine if theadditional drug therapy creates a potential drug interaction.

Next, in step 2110, code writes the encrypted PID and the set ofmedications to a file in which the data structure associates theencrypted PID with the set of medications.

The foregoing steps may be repeated for each unique encrypted PID infield 1302.

Alternatively, conventional SQL commands may be used to achieve the sameassociation of encrypted PIDs and medications.

At some point, code refers to a table or file in which differentinformational messages are associated with each encrypted PID set toassociate with the set of medications previously associated with thatencrypted PID.

Embodiment (J) is any algorithm applied to the foregoing data thatselects those de-identified patients who are late for a prescriptionrefill. This embodiment then uses an algorithm implemented with booleanlogic to determine if these patients should be reminded to continuetheir therapy.

FIG. 22 is a flowchart illustrating a method for utilizing the databaseschema and database tables of central server database 120A shown in FIG.9 to produce targeted informational messages for de-identified patientswho are late for a prescription refill.

In step 2202 code determines all NDCs associated with a specific patientusing data table 300B′.

Next, in step 2204, code determines a first set of all medicationsassociated with the first set of NDCs using data table 902.

Next, in step 2206, code selects those de-identified patients who arelate for a prescription refill using data in table 904.

Next, in step 2208, code determines if these de-identified patientsshould be reminded to continue their therapy.

Next, in step 2210, code writes the encrypted PID and the first set ofmedications to a file in which the data structure associates theencrypted PID with the first set of medications.

The foregoing steps may be repeated for each unique encrypted PID infield 1302.

Alternatively, conventional SQL commands may be used to achieve the sameassociation of encrypted PIDs and medications.

At some point, code refers to a table or file in which differentinformational messages are associated with each encrypted PID set toassociate with the set of medications previously associated with thatencrypted PID.

Embodiment (K) is any algorithm applied to the foregoing data thatselects those de-identified patients who are late for a prescriptionrefill. This embodiment then uses an algorithm implemented with booleanlogic to determine if these patients should be informed of othermedications used to treat the same condition that may work better forthem.

FIG. 22 is a flowchart illustrating a method for utilizing the databaseschema and database tables of central server database 120A shown in FIG.9 to produce targeted informational messages for de-identified patientswho are late for a prescription refill.

In step 2202 code determines all NDCs associated with a specific patientusing data table 300B′.

Next, in step 2204, code determines a first set of all medicationsassociated with the first set of NDCs using data table 902.

Next, in step 2206, code selects those de-identified patients who arelate for a prescription refill using data in table 904.

Next, in step 2208, code determines if these patients should be informedof other medications used to treat the same condition that may workbetter for them.

Next, in step 2210, code writes the encrypted PID and the first set ofmedications to a file in which the data structure associates theencrypted PID with the first set of medications.

The foregoing steps may be repeated for each unique encrypted PLD infield 1302.

Alternatively, conventional SQL commands may be used to achieve the sameassociation of encrypted PIDs and medications.

At some point, code refers to a table or file in which differentinformational messages are associated with each encrypted PID set toassociate with the set of medications previously associated with thatencrypted PID.

Embodiment (L) is any algorithm applied to the foregoing data thatselects those de-identified patients who have previously usedmedications to treat seasonal conditions. This embodiment then uses analgorithm implemented with boolean logic to determine if these patientscould benefit from similar medication therapy upon the next seasonalevent.

FIG. 23 is a flowchart illustrating a method for utilizing the databaseschema and database tables of central server database 120A shown in FIG.9 to produce targeted informational messages for de-identified patientswho have previously used medications to treat seasonal conditions.

In step 2302 code determines all NDCs associated with a specific patientusing data table 300B′.

Next, in step 2304, code determines a first set of all medicationsassociated with the first set of NDCs using data table 902.

Next, in step 2306, code selects those de-identified patients whopreviously used medications to treat seasonal conditions using data intable 904.

Next, in step 2308, code determines if these patients could benefit fromsimilar medication therapy upon the next seasonal event.

Next, in step 2310, code writes the encrypted PID and the first set ofmedications to a file in which the data structure associates theencrypted PID with the first set of medications.

The foregoing steps may be repeated for each unique encrypted PID infield 1302.

Alternatively, conventional SQL commands may be used to achieve the sameassociation of encrypted PIDs and medications.

At some point, code refers to a table or file in which differentinformational messages are associated with each encrypted PID set toassociate with the set of medications previously associated with thatencrypted PID.

Embodiment (M) is any algorithm applied to the foregoing data thatselects those de-identified patients who may be taking two drugs incombination. This embodiment then uses an algorithm implemented withboolean logic to determine if these patients could benefit from takingone drug containing both individual drug ingredients.

FIG. 24 is a flowchart illustrating a method for utilizing the databaseschema and database tables of central server database 120A shown in FIG.9 to produce targeted informational messages for de-identified patientswho may be taking two drugs in combination.

In step 2406, code selects those de-identified patients who may betaking two drugs in combination using data in table 904.

Next, in step 2408, code determines if these patients could benefit fromtaking one drug containing both individual drug ingredients.

Next, in step 2410, code writes the encrypted PID and the one drugcontaining both individual drug ingredients to a file in which the datastructure associates the encrypted PID with the one drug containing bothindividual drug ingredients.

Alternatively, conventional SQL commands may be used to achieve the sameassociation of encrypted PIDs and medications.

At some point, code refers to a table or file in which differentinformational messages are associated with each encrypted PID set toassociate with the set of medications previously associated with thatencrypted PID.

Embodiment (N) is any algorithm applied to the foregoing data thatselects those de-identified patients who exhibit non-compliantprescription refill behavior. This embodiment then uses an algorithmimplemented with boolean logic to determine if these patients couldbenefit from a refill reminder just prior to their prescription refilldue date.

FIG. 25 is a flowchart illustrating a method for utilizing the databaseschema and database tables of central server database 120A shown in FIG.9 to produce targeted informational messages for de-identified patientswho exhibit non-compliant prescription refill behavior.

In step 2502 code determines all NDCs associated with a specific patientusing data table 300B′.

Next, in step 2504, code determines a first set of all medicationsassociated with the first set of NDCs using data table 902.

Next, in step 2506, code determines selects those de-identified patientswho exhibit non-compliant prescription refill behavior using data intable 904.

Next, in step 2508, code determines if these patients could benefit froma refill reminder just prior to their prescription refill due date.

Next, in step 2510, code writes the encrypted PID and the first set ofmedications to a file in which the data structure associates theencrypted PID with the first set of medications.

The foregoing steps may be repeated for each unique encrypted PD infield 1302.

Alternatively, conventional SQL commands may be used to achieve the sameassociation of encrypted PIDs and medications.

At some point, code refers to a table or file in which differentinformational messages are associated with each encrypted PID set toassociate with the set of medications previously associated with thatencrypted PID.

In addition, the present invention may trigger the production ofinformational messages based on the following exemplary selection orfiltering criteria: age under 90; gender; payer identification; cashpayment; NDC; pill count; number of refills; refills remaining on theprescription; new or refill prescription; BIN (Bank IdentificationNumber); NCPDP (National Council for Prescription Drug Programs)provider ID, which is an (individual pharmacy identifier; and DEA (DrugEnforcement Administration) number (encrypted).

For example, using embodiments of the present invention, a selection orfiltering program can be designed to reach a patient populationundergoing a specific drug treatment protocol and which falls withindesired (specified) demographic and insurance parameters.

The present invention enables additional segmentation and targeting byusing, for example, a unique pharmacy outlet identifier (pharmacy orstore ID) and its geographic location as a proxy for a patient's homeaddress in those cases where the proxy address will not re-identify thepatient in conformance with HIPAA standards.

Using these targeted message criteria (also referred to as triggercriteria and as categorization and filtering criteria) in exemplaryembodiments allows the delivery of variable and highly relevantinformation to a large number of different patient groups. The presentinvention thereby provides sophisticated patient service functionalityby targeting highly relevant informational messages at specific groupsof patients.

With the present invention, it is not required that all pharmaciesprovide targeted messages resulting from any or all trigger criteria.Each pharmacy or store or set of stores commonly owned may select toimplement criteria of their choosing, for example, by marketingcategory, by manufacturer, or as a regulatory required message.

An advantage of the present invention is that the types of selectionprograms created reflect the drug information data available at anygiven moment in time.

Systems 120, 130 may collect and maintain a set of logs, which maycontain, for example, accounting information related to newsletterproduction. These logs, for example, may be used to support billingfunctions and may also be used in troubleshooting. The logs may beprocessed and loaded by data load system 510 and may reside on datawarehouse 514, for example.

The following de-identified data, for example, may be captured by thesystem of the present invention in logs: prescription number; NDC(National Drug Code); age under 90; gender; pill count; refill number;new or refill; and refills remaining.

Logs may be transmitted to central CS 120 daily, for example, overnight,and then may, for example, be maintained by central CS 120 for a periodof time (for example, 1 year) before being purged or may be maintainedindefinitely.

Log data preferably is de-identified and maintained secure fromunauthorized access. Log data can be aggregated to assess effectivenessrates for the advertising programs.

The inventors conceive of changes, for example, to the triggering,newsletter printing, and data logging processes, as needed.

In an embodiment, pharmacy management CS 130 combines a PID with apharmacy chain ID, a store ID and optionally a transaction ID to form acombination ID. The combination ID may be associated with the PID or theencrypted PID.

A third party's CS may hash a vendor specific value (using, for example,the SHA-1 algorithm) into PIDs used in that vendor's retail store. Thisencrypted data may then be maintained outside of the control of the userof the central system 120, for additional security.

The present invention advantageously allows HIPAA acceptable reducedlogging of pharmacy data in locations where population size is below20,000 for a 3-digit zip code and allows for the handling the age ofpatients 90 years old and above as 90 years old in those cases where thelogging information will not re-identify patients in conformance withHIPAA standards.

Embodiments recognize that since a store location can serve as areasonable proxy for a 5 digit zip code, that in the sparsely populatedareas that fall into this category the correlation is likely higher.Logging may therefore depend on zip code and assorted population sizesof store geographic locations. Central CS 120 may, for example,aggregate transaction data for all (presently 17) restricted 3 digit zipcodes into a single 3 digit zone 000.

Alternatively, for areas having small populations, PID information maynot be transmitted out of the corresponding pharmacy stores andinformation logged in any CS may exclude PID information. In thesealternatives, no informational messages are targeted based upon priortransaction history of an individual. Instead, information may betriggered in the pharmacy management CS by NDC, age, gender, or thelike, although age and gender may not be logged.

Embodiments may use time intervals between prescription filling dates asa surrogate for actual date data. This alternative provides the abilityto perform the desired analytics and correlations while minimizing therisk of re-identification. The time interval may be supplied by pharmacymanagement CS 130. Alternatively, central CS 120's software maycalculate the time interval before writing information to logs.

Embodiments may advantageously use data from physician offices toprovide, for example, helpful compliance dates (specifically aroundfirst fill rates).

Embodiments may advantageously link pharmacy and physician office datato allow maximal de-identified compliance solutions.

Embodiments may advantageously allow the addition of outside informationmodel components in a compliant de-identified method.

Embodiments may advantageously allow the handling large-scale,multi-source, patient-level information.

Embodiments may advantageously aggregate de-identified patient data todevelop additional service offerings.

The PID may be credit card numbers, pharmacy or health relatedidentification numbers, and any other identification used by a patient.

With the present invention, there is no way for an un-authorized thirdparty to determine a card holder's real identity even if central CS120's security is compromised. However, the present invention can stilltarget the card holder for targeted informational messages because everytime the retail store sees the customer's card number that number may beassociated with the customer's transaction data already stored inassociation with some unique encrypted PID.

Typically, credit card transactions to pay for pharmacy purchases andcorresponding pharmacy prescription order transactions are separate datatransactions, in the sense that the information transmitted from thepharmacy management CS in association with the credit card identifierdoes not contain product or service purchase information. Moreover, inpreferred embodiments, purchase of non pharmacy goods, such as purchasesfrom a supermarket and corresponding credit card identifier or otherpersonal identifier are stored in association with at least part of thecredit card identifier or other personal identifier in one datastructure, whereas purchases of pharmacy prescription products, arestored in separate data structures having no association between anyidentifiers in the two separate data structures. The inventors doconceive of, as a currently non-preferred embodiment, thede-identification and ID encryption process employable for both nonpharmacy retail store POS transactions and pharmacy transactions. Insuch an embodiment, both non-pharmacy and pharmacy transactions fortransaction inside of one retail store or in one retail store chain, maybe associated with one another, and still effectively maintain pharmacypatient anonymity.

Some embodiments shown in the figures illustrate a division ofprocessing among separate units or machines. This is not a requirementof the invention, and the various elements could be combined into fewermachines, be distributed among various machines differently, or, infact, be contained in a single machine with a single computer.Embodiments utilizing such redistributions can be designed bypractitioners in the relevant arts.

1. A computer implemented method for providing targeted informationalmessages to individuals, comprising: (a) a central computer systemreceiving from a remote computer system, first de-identified individualtransaction data for a first individual transaction and said centralcomputer system receiving an associated first encrypted PID associatedwith said first de-identified individual transaction data for said firstindividual transaction; (b) said central computer system storing, saidfirst de-identified individual transaction data in association with saidfirst encrypted PID, in a first individual transaction data record forsaid first individual transaction in a central computer system database;(c) said central computer system receiving from said remote computersystem second de-identified individual transaction data for a secondindividual transaction and said first encrypted PID associated with saidsecond de-identified individual transaction data for said secondindividual transaction; (d) said central computer system storing, saidsecond de-identified individual transaction data for said secondindividual transaction in association with said first encrypted PID, ina second individual transaction data record for said second individualtransaction in said central computer system database; (e) said centralcomputer storing targeting criteria in association with targetedinformational messages in said central computer system database; (f)said central computer system determining, from at least onede-identified individual transaction data stored in said centralcomputer system database in association with said first encrypted PIDand targeting criteria stored in said central computer system, a firsttargeting criterion of said targeting criteria satisfied by said atleast one de-identified individual transaction data stored in saidcentral computer system database in association with said firstencrypted PID; (g) wherein said first targeting criterion is stored inassociation with a first targeted informational message in said centralcomputer system database; and (h) subsequent to said central computersystem determining said first targeting criterion is satisfied by saidat least one de-identified individual transaction data stored inassociation with said first encrypted PID, said central computer systemtransmitting to another computer system said first targetedinformational message in association with said first encrypted PID. 2.The method of claim 1, wherein: said first targeting criterion specifiestwo or more medications that are used only for treatment of one diseasesuch that said two or more medications associated with an encrypted PIDindicates treatment of one disease of a corresponding patient; andwherein said first targeted informational message provides informationabout said one disease.
 3. The method of claim 1, wherein: said firsttargeting criterion specifies two or more medications purchased overtime associated with an encrypted PID indicating treatment of onedisease of a corresponding patient requiring additional treatment tomaintain or control disease progression; and wherein said first targetedinformational message provides information about said one disease. 4.The method of claim 1, wherein: said first targeting criterion specifiespurchase of a sequence of drugs over time associated with an encryptedPID indicating a stage of therapy of a corresponding patient; andwherein said first targeted informational message provides informationrelevant to said stage of therapy.
 5. The method of claim 1, wherein:said first targeting criterion specifies identification of medicationsassociated with an encrypted PID indicating existence in a correspondingpatient of one or more risk factors for a specific disease and existenceof no medications used to treat said specific disease; and wherein saidfirst targeted informational message provides information relevant tosaid specific disease.
 6. The method of claim 1, wherein: said firsttargeting criterion specified medications and time intervals associatedwith an encrypted PID indicating compliance with or persistence overtime by a corresponding patient of a medication regimen.
 7. The methodof claim 1, wherein: said first targeting criterion specifies differentmedications associated with an encrypted PID, indicating a switch by acorresponding patient in drugs used to treat a specific disease from onemedication within a first drug class to another medication within saidfirst drug class or another medication in a second drug class known totreat said specific disease.
 8. The method of claim 1, wherein: saidfirst targeting criterion specifies medications and time periods betweenpurchases of said medications associated with an encrypted PIDindicating chronic treatment of a specific disease in a correspondingpatient.
 9. The method of claim 1, wherein: said first targetingcriterion specifies additional drug therapies which contra indicatecontra indicate use of a known drug, and said known drug; and whereinsaid first targeted informational message identifies said contraindication with use of said known drug.
 10. The method of claim 1,wherein: said first targeting criterion identifies prescription refilldates for which no corresponding prescription data exists for anencrypted PID; and wherein said first targeted informational messagecontains a reminder to continue therapy relating to the prescribedmedicine.
 11. The method of claim 1, wherein: said first targetingcriterion identifies prescription refill dates for a prescription forwhich no corresponding prescription data exists for an encrypted PID;and wherein said first targeted informational message identifies atleast one other medication used to treat the same condition as themedicine contained in said prescription.
 12. The method of claim 1,wherein: said first targeting criterion identifies encrypted PIDsassociated with said at least one de-identified transaction dataindicating treatment for a seasonal condition that; and wherein saidfirst targeted informational message identifies at least one medicationused to treat said season condition.
 13. The method of claim 1, wherein:said first targeting criterion identifies two drugs products associatedwith an encrypted PID, for which there is a single drug productcontaining drugs in said two drug products; wherein said first targetedinformational message identifies said single drug product.
 14. Themethod of claim 1, wherein: said first targeting criterion identifiesPIDs of patients who may benefit from a refill reminder just prior totheir prescription refill due date as identified by previousnon-compliant prescription refill behavior, and said first targetingcriterion also includes time just prior to refill due date, and; whereinsaid first targeted informational message provides refill reminder. 15.A computer implemented system for providing targeted informationalmessages to individuals, comprising: (a) a central computer systemprogrammed to receive from a remote computer system, first de-identifiedindividual transaction data for a first individual transaction and anassociated first encrypted PID associated with said first de-identifiedindividual transaction data for said first individual transaction; (b)said central computer system programmed to store, said firstde-identified individual transaction data in association with said firstencrypted PID, in a first individual transaction data record for saidfirst individual transaction in a central computer system database; (c)said central computer system programmed to receive, from said remotecomputer system, second de-identified individual transaction data for asecond individual transaction and said first encrypted PID associatedwith said second de-identified individual transaction data for saidsecond individual transaction; (d) said central computer systemprogrammed to store, said second de-identified individual transactiondata for said second individual transaction in association with saidfirst encrypted PID, in a second individual transaction data record forsaid second individual transaction in said central computer systemdatabase; (e) said central computer programmed to store targetingcriteria in association with targeted informational messages in saidcentral computer system database; (f) said central computer systemprogrammed to determine, from at least one de-identified individualtransaction data stored in said central computer system database inassociation with said first encrypted PID and targeting criteria storedin said central computer system, a first targeting criterion of saidtargeting criteria satisfied by said at least one de-identifiedindividual transaction data stored in said central computer systemdatabase in association with said first encrypted PID; (g) wherein saidfirst targeting criterion is stored in association with a first targetedinformational message in said central computer system database; and (h)only if said central computer system has determined that said firsttargeting criterion is satisfied by said at least one de-identifiedindividual transaction data stored in association with said firstencrypted PID, said central computer system is programmed to transmit toanother computer system, said targeted information message inassociation with said first encrypted PID.